arXiv AI August 21, 2026 By Roshan Kenia, Stephanie L McNamara, William Lotter

Anatomy Contextualized Adaptation of CT Foundation Models

arXiv:2607. 27154v2 Announce Type: replace-cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.

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arXiv AI

Jul 31

Anatomy Contextualized Adaption of CT Foundation Models

arXiv:2607. 27154v1 Announce Type: cross Abstract: CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals.

By Roshan Kenia, Stephanie L McNamara, William Lotter

llms rag multimodal safety

Hugging Face Trending Papers

Jun 24

Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography

Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle with 3D CT imaging due to inefficient visual backbones and coarse semantic alignment. To address these issues, we propose a tailored VLP framework featuring three key components: (1) a CNN-ViT hybrid encoder that replaces ViT's patch embedding with a 3D CNN backbone to efficiently capture local anatomical details while preserving global attention and compatibility with pre-trained cross-modal priors; (2) a disease-level contrastive learning mechanism using learnable query tokens to dynamically extract disease-specific semantics from full reports and align them with corresponding visual features, thereby disentangling distinct diseases within the same anatomical region; and (3) a diagnosis-aware prompt strategy that employs real clinical phrases and aggregated disease prototypes to bridge the pre-training-inference gap and enhance zero-shot diagnostic reliability.

rag multimodal benchmarks safety

arXiv Machine Learning

Jun 3

GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations

arXiv:2606. 03180v1 Announce Type: cross Abstract: Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows.

By Jonggwon Park, Seongeun Lee, Junhyun Park, Hannah Yun, Hyunwoong Kim, Sohyun Jeong, Hyewon Kang, Byungmu Yoon, Kyoyun Choi

llms rag computer-vision efficiency multimodal safety

arXiv AI

Aug 6

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608. 04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis.

By Zhenyu Yi, Jianwei Xu, Yue Hu, Zhongwei Qiu, Sijing Li, Liang Huang, Bin Lv, Ling Zhang, Yingda Xia

efficiency multimodal safety

arXiv Computer Vision

Aug 25

Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation

arXiv:2409.16183v2 Announce Type: replace Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...

By Xiaohong Liu, Guoxing Yang, Yulin Luo, Jiaji Mao, Xiang Zhang, Haibo Wang, Zhiyang He, Ming Gao, Shanghang Zhang, Jun Shen, Guangyu Wang

multimodal benchmarks

arXiv Computer Vision

1d ago

ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT

arXiv:2608.28455v1 Announce Type: new Abstract: Contrastive vision-language learning uses paired chest CT volumes and radiology reports to learn abnormality classifiers without manually annotated lab...

By Huseyin Umut Isik, Mehmet Alp Ozaydin, Sila Kurugol, \c{S}eyda Ertekin